[bigstrat2003]

This is absolutely terrible advice. You should never ever use LLMs to work on something you don't understand already, because you have no way to catch the machine when it screws up (and it will screw up). Just like with every other form of automation before LLMs, a smart person only automates things he already knows how to do himself.

[xlii]

"Only a Sith deals in absolutes" ;-)

I mostly agree it's an area that's risky to wander into mindlessly but it is much more easier to validate knowledge than to practice it.

E.g. I can't write Chinese but can validate if piece of Chinese is a valid one (by feeding to N translators, other LLMs or asking a friend who knows Chinese).

Under assumption of "LLM output is false until proven otherwise" it's not a bad approach and worked for me in various scenarios. (E.g. I asked for implementation of algorithm in Rust and then validated it against base definition).

[mettamage]

LLMs allow for play. Play means learning. Then that knowledge can be utilized for a second project or to chat with someone more experienced.

We all have different learning styles. I learn through play when it comes to LLMs.

[johnsmith1840]

Yeah no. Getting the first hello world up is more important than anything else.

Until you physically see it running learning is slow.

I learned k8s through many months of study and pain pre AI. Once I actually got it up learning was FAR easier.

This is like using a jupyter notebook to learn python and is always the first thing I point to for someone just starting to learn. Only after should you learn venv, pip install, classes ect.

100% use AI to get started on something you don't understand. I will literally never start to learn about a technical system again without first doing a hello world with AI.